July 27, 2012 – There are perceptions of how well a health care organization is operating and then there is the reality of data presented in a manner that is easy to find and understand. Presentation is the beauty of dashboard technology for executives, according to Andrew Proctor, M.D., senior director of business intelligence in the medical operations division of Cleveland Clinic.
And while the clinic has lots of money, business intelligence is a journey that is doable even for small organizations over time, Proctor said during Health Data Management’s Healthcare Analytics Symposium in Chicago. The clinic started its journey toward better understanding operational indicators in 1993, but it wasn’t until getting dashboards in 2006 that the indicators became really useful.
The dashboards provide a menu of indicators, marked in red, yellow or green to indicate how well they are being met. One of the early indicators analyzed was clinic access – the waiting time to get into Cleveland Clinic. There was a perception that patients had to “know somebody” to get in and see a physician.
Business intelligence staff went to each chair in each department and asked how much time a week they would expect their physicians to be in an outpatient setting. Then they analyzed the data in Cleveland Clinic’s Epic system and found that often times, physicians spent much less time in outpatient care than the chairs believed. The results showed up in dashboard reports where green numbers showed how many hours are available to patients each week and black shows how many actual hours that physicians are in an ambulatory setting.
The next step was to see how long it takes 50 percent of new patients to have their first visit, which started with several weeks and now stands at seven to nine days. Data analysis found another important indicator, Proctor said. As wait times go down, patient satisfaction increases.
Cleveland Clinic also has used analytics to reduce infection rates and decrease blood utilization in intensive care units. Infection rates have dropped 50 percent over three years and nurses take pride in who has the best performance in protecting their patients from infection, Proctor said. Using data analytics to better understand utilization of blood, a limited resource and prone to infection risks, has significantly cut utilization even as the case mix slightly increased, he added.
With higher reliance on analytics comes the need for governance of the resource, Proctor advised. “We now have well over 100 dashboard requests in our queue and need to prioritize.” The analytics department started with a staff of six and now has 40. “We are a center for excellence,” he added. “It was not the plan but kind of evolved that way.” The lesson: An organization needs strategic decisions to come from executives to ensure analysts are not spending time on requests that don’t have sufficient value.
This story originally appeared at Health Data Management.